A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics

Several state-of-the-art techniques – a neural network, Bayesian neural network, support vector machine and naive Bayesian classifier – are experimentally evaluated in discriminating fluorescencein situ hybridisation (FISH) signals. Highly-accurate classification of valid signals and artifacts of several cytogenetic probes (colours) is required for detecting abnormalities in FISH images. More than 3100 FISH signals are classified by each of the techniques into colour and as real or artifact with accuracies of around 98% and 88%, respectively. The results of the comparison also show a trade-off between simplicity represented by the naive Bayesian classifier, and high classification performance represented by the other techniques.

[1]  Vladimir Vapnik,et al.  Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .

[2]  S. Gull Bayesian Inductive Inference and Maximum Entropy , 1988 .

[3]  R. T. Cox Probability, frequency and reasonable expectation , 1990 .

[4]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[5]  David J. C. MacKay,et al.  A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.

[6]  Geoffrey E. Hinton,et al.  Keeping the neural networks simple by minimizing the description length of the weights , 1993, COLT '93.

[7]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[8]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[9]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[10]  H. Netten,et al.  Fluorescent dot counting in interphase cell nuclei , 1996 .

[11]  J. C. BurgesChristopher A Tutorial on Support Vector Machines for Pattern Recognition , 1998 .

[12]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[13]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[14]  Charles M. Bishop,et al.  Ensemble learning in Bayesian neural networks , 1998 .

[15]  B. Lerner,et al.  Feature representation for the automatic analysis of fluorescence in-situ hybridization images , 1999 .

[16]  Boaz Lerner,et al.  Gelfish – graphical environment for labelling FISH images , 1999 .

[17]  Neil D. Lawrence,et al.  A Variational B ayesian Committee of Neural Networks , 1999 .

[18]  S Dhanjal,et al.  Automatic signal classification in fluorescence in situ hybridization images. , 2001, Cytometry.